Software Pipeline for "Implementing landscape genetics in molecular epidemiology to determine drivers of vector-borne disease: A malaria case study"

This is the software necessary to reproduce the analysis for the paper Implementing landscape genetics in molecular epidemiology to determine drivers of vector-borne disease: A malaria case study, published in Molecular Ecology. This entry contains the Git repositories for 1) the code specific to this manuscript (note that the year is incorrect in the file name), 2) my hubutil R package, 3) my resutil R package, and 4) my geoproc package of Python scripts. All four are required to reproduce the results presented in this manuscript. At time of publication, these repositories are also available on BitBucket at https://bitbucket.org/a-hubbard/hubbardetal_landgen_drivers_malaria/, https://bitbucket.org/a-hubbard/hubutil/, https://bitbucket.org/a-hubbard/resutil/, and https://bitbucket.org/a-hubbard/geoproc/, respectively. With this code and the datasets included as supplemental tables to the publication, it should be possible to completely reproduce our analysis. For details on installing and running this pipeline, see the README.md in hubbardetal_molecol_2022.zip